Neural Networks - Module Project-I | Signal Strength Regressor


A communications equipment manufacturing company has a product which is responsible for emitting informative signals. Company wants to build a machine learning model which can help the company to predict the equipment’s signal quality using various parameters.

The data set contains information on various signal tests performed:
1. Parameters: Various measurable signal parameters.
2. Signal_Quality: Final signal strength or quality

We will build a regressor which can use these parameters to determine the signal strength or quality [as number].


Uni-Variate Analysis

Parameter 1, 8, 9, 10 and 11 seem to have fairly normal distributions. Others do not seem to follow any distributions

Bi-Variate Analysis

Our dataset has outliers as per the boxplots

From the correlations:

  • we can see that parameter 1 and parameter 9 have a high correlation of -0.68
  • parameter 2 and parameter 3 have a correlation of -0.55
  • parameter 3 and parameter 9 have a correlation of -0.54
  • Parameters 4, 6 and 9 have almost negligible correlation with Signal_Strength
    Hence, These parameters could be considered as noise and dropped during modeling

  • Train a vanilla random forest model to get feature importances:

    Hence, Looking at the correlations between the features and the target and the feature importances found from trainign a plain vanilla random forest model.

  • We remove parameters 1, 9 due to it's high correlation with other parameters and negligible correlation with target variable
  • We select Parameters: 11, 10, 2, 7, 8, 5, 3 for modeling.

  • Hence, Looking at the correlations after feature selection

  • The spearman correlations which we created after removing highly correlated features and selecting features using feature importances do not show any highly correlated feature.